[jira] [Updated] (SPARK-17963) Add examples (extend) in each function and improve documentation with arguments

["Xiao Li (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-17963?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Xiao Li updated SPARK-17963:
----------------------------
    Assignee: Hyukjin Kwon

> Add examples (extend) in each function and improve documentation with arguments
> -------------------------------------------------------------------------------
>
>                 Key: SPARK-17963
>                 URL: https://issues.apache.org/jira/browse/SPARK-17963
>             Project: Spark
>          Issue Type: Documentation
>          Components: SQL
>            Reporter: Hyukjin Kwon
>            Assignee: Hyukjin Kwon
>             Fix For: 2.1.0
>
>
> Currently, it seems function documentation is inconsistent and does not have examples ({{extend}} much.
> For example, some functions have a bad indentation as below:
> {code}
> spark-sql> DESCRIBE FUNCTION EXTENDED approx_count_distinct;
> Function: approx_count_distinct
> Class: org.apache.spark.sql.catalyst.expressions.aggregate.HyperLogLogPlusPlus
> Usage: approx_count_distinct(expr) - Returns the estimated cardinality by HyperLogLog++.
>     approx_count_distinct(expr, relativeSD=0.05) - Returns the estimated cardinality by HyperLogLog++
>       with relativeSD, the maximum estimation error allowed.
> Extended Usage:
> No example for approx_count_distinct.
> {code}
> {code}
> spark-sql> DESCRIBE FUNCTION EXTENDED count;
> Function: count
> Class: org.apache.spark.sql.catalyst.expressions.aggregate.Count
> Usage: count(*) - Returns the total number of retrieved rows, including rows containing NULL values.
>     count(expr) - Returns the number of rows for which the supplied expression is non-NULL.
>     count(DISTINCT expr[, expr...]) - Returns the number of rows for which the supplied expression(s) are unique and non-NULL.
> Extended Usage:
> No example for count.
> {code}
> whereas some do have a pretty one
> {code}
> spark-sql> DESCRIBE FUNCTION EXTENDED percentile_approx;
> Function: percentile_approx
> Class: org.apache.spark.sql.catalyst.expressions.aggregate.ApproximatePercentile
> Usage:
>       percentile_approx(col, percentage [, accuracy]) - Returns the approximate percentile value of numeric
>       column `col` at the given percentage. The value of percentage must be between 0.0
>       and 1.0. The `accuracy` parameter (default: 10000) is a positive integer literal which
>       controls approximation accuracy at the cost of memory. Higher value of `accuracy` yields
>       better accuracy, `1.0/accuracy` is the relative error of the approximation.
>       percentile_approx(col, array(percentage1 [, percentage2]...) [, accuracy]) - Returns the approximate
>       percentile array of column `col` at the given percentage array. Each value of the
>       percentage array must be between 0.0 and 1.0. The `accuracy` parameter (default: 10000) is
>        a positive integer literal which controls approximation accuracy at the cost of memory.
>        Higher value of `accuracy` yields better accuracy, `1.0/accuracy` is the relative error of
>        the approximation.
> Extended Usage:
> No example for percentile_approx.
> {code}
> Also, there are several inconsistent indentation, for example, {{_FUNC_(a,b)}} and {{_FUNC_(a, b)}} (note the indentation between arguments.
> It'd be nicer if most of them have a good example with possible argument types.
> Suggested format is as below for multiple line usage:
> {code}
> spark-sql> DESCRIBE FUNCTION EXTENDED rand;
> Function: rand
> Class: org.apache.spark.sql.catalyst.expressions.Rand
> Usage:
>       rand() - Returns a random column with i.i.d. uniformly distributed values in [0, 1].
>         seed is given randomly.
>       rand(seed) - Returns a random column with i.i.d. uniformly distributed values in [0, 1].
>         seed should be an integer/long/NULL literal.
> Extended Usage:
> > SELECT rand();
>  0.9629742951434543
> > SELECT rand(0);
>  0.8446490682263027
> > SELECT rand(NULL);
>  0.8446490682263027
> {code}
> For single line usage:
> {code}
> spark-sql> DESCRIBE FUNCTION EXTENDED date_add;
> Function: date_add
> Class: org.apache.spark.sql.catalyst.expressions.DateAdd
> Usage: date_add(start_date, num_days) - Returns the date that is num_days after start_date.
> Extended Usage:
> > SELECT date_add('2016-07-30', 1);
>  '2016-07-31'
> {code}



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